Annual violent right-wing attacks (5-year groupings)

packages <- c("tidyverse", "sf", "readxl", "janitor",
              "tidycensus", "viridis",  "lubridate",
              "ggfx", "albersusa")

if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org")  
}


library(tidyverse)
library(sf)
library(readxl)
library(janitor)
library(tidycensus)
library(viridis)
library(lubridate)
library(ggfx)
library(albersusa)

options(tigris_use_cache = TRUE, tigris_class = "sf")

addUnits <- function(n) {
  labels <- ifelse(n < 1000, n,  # less than thousands
                   ifelse(n < 1e6, paste0(round(n/1e3), 'k'),  # in thousands
                          ifelse(n < 1e9, paste0(round(n/1e6), 'M'),  # in millions
                                 ifelse(n < 1e12, paste0(round(n/1e9), 'B'), # in billions
                                        ifelse(n < 1e15, paste0(round(n/1e12), 'T'), # in trillions
                                               'too big!'
                                        )))))
  return(labels)
}

# Questions ----

# Request this raw data from CSIS
#df2 <- read_excel("../../data/raw_data/CSIS_TNT_Terrorism_US_Jan94-Jan21_Mar2021.xlsx")


df2 <- read_csv("../../data/clean_data/csis_wapo_domestic_terrorism.csv")

df2 <- clean_names(df2)

df2 <- df2 %>% 
  rename(tnt_orientation=orientation_csis,
         year=year_csis,
         state=state_csis,
         month=month_csis,
         vict_killed=vict_killed_csis,
         weapon=weapon_csis,
         target=target_csis,
         lat=lat_csis,
         long=long_csis)

# set your census api key

# census_api_key("YOUR API KEY GOES HERE")

county_pov <- get_acs(geography = "county",
                      variables = "B17001_002",
                      summary_var = "B17001_001",
                      geometry = TRUE,
                      shift_geo = TRUE) %>% 
  mutate(pctpov = 100 * (estimate/summary_est))

right_spatial <- filter(df2, tnt_orientation=="Violent Far-right") %>% 
  filter(!is.na(lat)) %>% 
  st_as_sf(coords=c("long", "lat"), crs = "+proj=longlat") %>% 
  st_transform(crs=st_crs(county_pov))

right <- st_coordinates(right_spatial$geometry) %>% 
  data.frame()
colnames(right) <- c("long", "lat") 

right_spatial <- cbind(right_spatial, right)
#saveRDS(county_pov, "../../data/clean_data/county_pov.RDS")
#saveRDS(right_spatial, "../../data/clean_data/right_spatial.RDS")

right_spatial_filtered <- right_spatial %>% 
  filter(year!=1994) %>% 
  mutate(year_group=case_when(
    year >= 1995 & year <2000 ~ "1995-1999",
    year >= 2000 & year <2005 ~ "2000-2004",
    year >= 2005 & year <2010 ~ "2005-2009",
    year >= 2010 & year <2015 ~ "2010-2014s",
    year >= 2015 & year <2020 ~ "2015-2019",
    year >= 2020 ~ "2020-2021",
  )) %>% 
  count(year_group, lat, long)
  

us_sf <- usa_sf("laea")

us_sf  %>% 
ggplot(aes()) +
    darklyplot::theme_dark2()+

  #geom_sf(aes(fill = pctpov), color=NA) +
   geom_sf(fill = "grey", alpha=.8, color="#ffffff") +
     with_inner_glow(
       geom_sf(fill="black", color = "#ffffff"),
     colour = 'red',
     sigma = 4
   )+ 
  #geom_sf(fill = "white", color=NA) +
    with_outer_glow(
    with_inner_glow(geom_point(data=right_spatial_filtered,
             aes(x=long, y=lat), size=right_spatial_filtered$n*2, color="gold"),
      colour="white", sigma=2),
    colour="gold", sigma=3,expand=1) +
  coord_sf(datum=NA) +
  facet_wrap(~year_group, ncol=1) +
  labs(title = "Violent Far-right incidents over time",
       subtitle = "",
       caption = "Source: CSIS",
       y="", x="") +
  scale_fill_viridis(direction=-1)

2020 and 2021

right_spatial_filtered <- right_spatial_filtered %>% 
  filter(year_group=="2020-2021")

protests_counties <- read_csv("../../data/clean_data/protests_counties_2020.csv")

county_pov <- left_join(county_pov, protests_counties)


st_list <- protests_counties %>% 
  filter(!is.na(attendance_racial_just)) %>% 
  select(GEOID) %>% 
  pull(GEOID)

paint_map <- function(st="CA",mycolor="red"){
  with_inner_glow(geom_sf(fill = "white",
                          data= .%>% filter(GEOID==st),
                          color = "#ffffff"),
                  colour=mycolor,sigma=5)
}

county_pov  %>% 
ggplot(aes()) +
    darklyplot::theme_dark2()+
  #geom_sf(aes(fill = pctpov), color=NA) +
   geom_sf(fill = NA, alpha=.8, color="#ffffff") +
   geom_sf(aes(fill=protests_racial_just)) +
   #geom_sf(aes(fill=protests_right_wing)) +
   #geom_sf(aes(fill=attendance_racial_just)) +
  geom_sf(data=us_sf,fill=NA, color="black") +
  #geom_sf(fill = "white", color=NA) +
   geom_point(data=right_spatial_filtered,
             aes(x=long, y=lat), size=right_spatial_filtered$n*2, color="red") +
  coord_sf(datum=NA) +
  #facet_wrap(~year_group, ncol=1) +
  labs(title = "Violent Far-right incidents in 2020 and 2021",
       subtitle = "Compared to number of racial justice protests",
       caption = "Source: CSIS, CountLove",
       y="", x="") +
  scale_fill_viridis(direction=-1)